{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from gs_quant.instrument import FXBinary, FXMultiCrossBinary, FXMultiCrossBinaryLeg\n",
    "from gs_quant.markets import PricingContext\n",
    "from gs_quant.risk import FXSpot\n",
    "from gs_quant.session import GsSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will screen for attractive dual binaries in FX to express a leveraged view on a potential vaccine led COVID recovery. This is an example of the GS Quant functionalities and it is not a recommendation of a trade in the instruments discussed, please follow up with your salesperson to discuss specific implementations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Define a few functions and inputs\n",
    "\n",
    "Let's start by defining a few functions and inputs to improve the readability of the notebook.\n",
    "\n",
    "First, let's define a tenor and our targets for various FX crosses. In this example, we are leveraging GS GIR's work for analyzing FX price targets in the event of an approved EUA of a COVID vaccine by the end of the year ([source](https://marquee.gs.com/content/research/en/reports/2020/11/08/e8589bbb-cc23-4480-859a-000a8014bd1d.html)). However, the framework we build in this notebook could be deployed to your own targets on vaccine developments (or any other relevant themes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# note that we are representing FX crosses as strings in mathematical notation (e.g. USD per GBP)\n",
    "targets = {\n",
    "    'USD/GBP': (1.34, 'call'),\n",
    "    'USD/AUD': (0.75, 'call'),\n",
    "    'USD/EUR': (1.20, 'call'),\n",
    "    'CAD/USD': (1.28, 'put'),\n",
    "    'JPY/USD': (104.1, 'call'),\n",
    "    'KRW/USD': (1099, 'put'),\n",
    "    'BRL/USD': (5.26, 'put'),\n",
    "    'MXN/USD': (19.87, 'put')\n",
    "}\n",
    "\n",
    "tenor = '2m'\n",
    "crosses = targets.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll define a few utility functions we'll use throughout the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def build_binary(tenor, cross, strike, opt_type, size=10e6, denominated='USD', buy_sell='Buy'):\n",
    "    '''\n",
    "    utility method to construct a FXBinaryBuilder\n",
    "    '''\n",
    "    return FXBinary(\n",
    "        buy_sell=buy_sell,\n",
    "        expiration_date=tenor,\n",
    "        option_type=opt_type,\n",
    "        notional_currency=denominated,\n",
    "        pair=cross,\n",
    "        strike_price=strike,\n",
    "        premium=0, \n",
    "        notional_amount=size,\n",
    "        payment_date=tenor\n",
    "    )\n",
    "\n",
    "\n",
    "def build_dual_binary(tenor, legs, size=10e6, denominated='USD', buy_sell='Buy'):\n",
    "    '''\n",
    "    utility method to construct an MCB builder\n",
    "    '''\n",
    "    built_legs = []\n",
    "    for leg in legs:\n",
    "        built_legs.append(FXMultiCrossBinaryLeg(\n",
    "            strike_price=leg['strike'], pair=leg['cross'], option_type='Binary %s' % leg['opt_type']\n",
    "        ))\n",
    "    return FXMultiCrossBinary(buy_sell, tenor, legs=tuple(built_legs), premium=0, notional_amount=size,\n",
    "                              notional_currency=denominated)\n",
    "\n",
    "\n",
    "\n",
    "def plot_heatmap(data, cmap, ylabel, xlabel, title):\n",
    "    '''\n",
    "    Utility function to build a heatmap\n",
    "    '''\n",
    "    plt.subplots(figsize=(16, 10))\n",
    "\n",
    "    vmax = data.max().max()\n",
    "    vmin = data.min().min()\n",
    "\n",
    "    ax = sns.heatmap(data, annot=True, vmin=vmin, vmax=vmax, fmt='.2f', cmap=cmap)\n",
    "    ax.set(ylabel=ylabel, xlabel=xlabel, title=title)\n",
    "    ax.xaxis.tick_top()\n",
    "    ax.xaxis.set_label_position('top')\n",
    "    \n",
    "    \n",
    "def corr(mcb_price, p1, p2):\n",
    "    '''\n",
    "    compute the correlation implied by the dual binary price and individuals\n",
    "    the sign is just the correlation between the payouts (so negative always indicates higher savings regardless of call/put combo)\n",
    "    '''\n",
    "    return (mcb_price - p1 * p2) / np.sqrt(p1 * (1 - p1) * p2 * (1 - p2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Vanilla Binaries\n",
    "\n",
    "First, a quick example that shows how to compute  the percent price of a binary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5046423444297603"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary = build_binary('2m', 'JPY/USD', 104, 'Put')\n",
    "binary.price() / 10e6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the vanilla binary prices for each of our individual targets to get a sense of the probability the market is assigning to these targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "vanilla_binaries = pd.DataFrame(index=crosses, columns=['spot_ref', 'target', 'price', 'price_future', 'risk_future'])\n",
    "\n",
    "with PricingContext(is_batch=True):\n",
    "    for cross, (strike, opt_type) in targets.items():\n",
    "        binary = build_binary(tenor, cross, strike, opt_type)\n",
    "        \n",
    "        vanilla_binaries.loc[cross, 'price_future'] = binary.price()\n",
    "        vanilla_binaries.loc[cross, 'risk_future'] = binary.calc(FXSpot)       \n",
    "        vanilla_binaries.loc[cross, 'target'] = strike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>spot_ref</th>\n",
       "      <th>target</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CAD/USD</th>\n",
       "      <td>1.30665</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.215593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USD/AUD</th>\n",
       "      <td>0.73208</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.255526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USD/EUR</th>\n",
       "      <td>1.1867</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.318962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MXN/USD</th>\n",
       "      <td>20.2446</td>\n",
       "      <td>19.87</td>\n",
       "      <td>0.36556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRW/USD</th>\n",
       "      <td>1105.25</td>\n",
       "      <td>1099</td>\n",
       "      <td>0.426555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USD/GBP</th>\n",
       "      <td>1.3301</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.456705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRL/USD</th>\n",
       "      <td>5.3062</td>\n",
       "      <td>5.26</td>\n",
       "      <td>0.466285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPY/USD</th>\n",
       "      <td>103.745</td>\n",
       "      <td>104.1</td>\n",
       "      <td>0.478362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        spot_ref target     price\n",
       "CAD/USD  1.30665   1.28  0.215593\n",
       "USD/AUD  0.73208   0.75  0.255526\n",
       "USD/EUR   1.1867    1.2  0.318962\n",
       "MXN/USD  20.2446  19.87   0.36556\n",
       "KRW/USD  1105.25   1099  0.426555\n",
       "USD/GBP   1.3301   1.34  0.456705\n",
       "BRL/USD   5.3062   5.26  0.466285\n",
       "JPY/USD  103.745  104.1  0.478362"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# once all results are computed summarize relevant details\n",
    "for cross in crosses:\n",
    "    vanilla_binaries.loc[cross, 'price'] = vanilla_binaries.loc[cross, 'price_future'].result() / 10e6\n",
    "    vanilla_binaries.loc[cross, 'spot_ref'] = vanilla_binaries.loc[cross, 'risk_future'].result()\n",
    "        \n",
    "vanilla_binaries[['spot_ref', 'target', 'price']].sort_values('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 3 - Dual Binaries\n",
    "\n",
    "Now calculate the price for ever combination of dual binaries struck at the targets defined above. The top row displays the cheapest dual binary by price, with the right column indicating the savings to the cheapest digi. Darker colors indicate potentially higher savings and therefore potentially more attractive dual digis.\n",
    "\n",
    "Please note that the payout of dual binaries is not guaranteed and is dependent on the level of FX spot in both crosses at the time of expiry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dual_binary_dict = []\n",
    "with PricingContext(is_batch=True):\n",
    "    # iterate over all 2-cross combinations\n",
    "    for idx, (cross1, cross2) in enumerate(itertools.combinations(crosses, 2)):\n",
    "        # build the dual binary\n",
    "        legs = [\n",
    "            {'cross': cross1, 'strike': targets[cross1][0], 'opt_type': targets[cross1][1]},\n",
    "            {'cross': cross2, 'strike': targets[cross2][0], 'opt_type': targets[cross2][1]}\n",
    "        ]\n",
    "        mcb = build_dual_binary(tenor, legs)\n",
    "\n",
    "        dual_binary_dict.append({\n",
    "            'cross1': cross1,\n",
    "            'cross2': cross2,            \n",
    "            'price_future': mcb.price(),\n",
    "        })       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in dual_binary_dict:\n",
    "    row['price'] = row['price_future'].result() / 10e6\n",
    "    \n",
    "    # individual prices\n",
    "    row['individual_1'] = vanilla_binaries.loc[row['cross1'], 'price']\n",
    "    row['individual_2'] = vanilla_binaries.loc[row['cross2'], 'price']\n",
    "    \n",
    "    row['corr'] = corr(row['price'], row['individual_1'], row['individual_2'])\n",
    "    # define savings as the % savings from the cheapest individual binary\n",
    "    row['savings'] = 1 - row['price'] / min(row['individual_1'], row['individual_2'])\n",
    "    \n",
    "    \n",
    "dual_binaries = pd.DataFrame(dual_binary_dict)[['cross1', 'cross2', 'price', 'savings', 'corr', 'individual_1', 'individual_2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Format the results, showing only the 10 dual binaries with the absolute cheapest mid prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    #T_3eeb7a9e_2a4f_11eb_8195_00505612051frow0_col3 {\n",
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       "            color:  #f1f1f1;\n",
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       "            background-color:  #023858;\n",
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       "            background-color:  #1379b5;\n",
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       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051f\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >cross1</th>        <th class=\"col_heading level0 col1\" >cross2</th>        <th class=\"col_heading level0 col2\" >price</th>        <th class=\"col_heading level0 col3\" >savings</th>        <th class=\"col_heading level0 col4\" >corr</th>        <th class=\"col_heading level0 col5\" >individual_1</th>        <th class=\"col_heading level0 col6\" >individual_2</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row0\" class=\"row_heading level0 row0\" >18</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow0_col0\" class=\"data row0 col0\" >CAD/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow0_col1\" class=\"data row0 col1\" >JPY/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow0_col2\" class=\"data row0 col2\" >7.3%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow0_col3\" class=\"data row0 col3\" >65</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow0_col4\" class=\"data row0 col4\" >-14.5%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow0_col5\" class=\"data row0 col5\" >21.6%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow0_col6\" class=\"data row0 col6\" >47.8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row1\" class=\"row_heading level0 row1\" >9</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow1_col0\" class=\"data row1 col0\" >USD/AUD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow1_col1\" class=\"data row1 col1\" >JPY/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow1_col2\" class=\"data row1 col2\" >7.4%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow1_col3\" class=\"data row1 col3\" >70</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow1_col4\" class=\"data row1 col4\" >-21.9%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow1_col5\" class=\"data row1 col5\" >25.6%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow1_col6\" class=\"data row1 col6\" >47.8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row2\" class=\"row_heading level0 row2\" >14</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow2_col0\" class=\"data row2 col0\" >USD/EUR</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow2_col1\" class=\"data row2 col1\" >JPY/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow2_col2\" class=\"data row2 col2\" >8.7%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow2_col3\" class=\"data row2 col3\" >72</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow2_col4\" class=\"data row2 col4\" >-28.1%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow2_col5\" class=\"data row2 col5\" >31.9%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow2_col6\" class=\"data row2 col6\" >47.8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row3\" class=\"row_heading level0 row3\" >21</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow3_col0\" class=\"data row3 col0\" >CAD/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow3_col1\" class=\"data row3 col1\" >MXN/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow3_col2\" class=\"data row3 col2\" >12.4%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow3_col3\" class=\"data row3 col3\" >42</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow3_col4\" class=\"data row3 col4\" >22.9%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow3_col5\" class=\"data row3 col5\" >21.6%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow3_col6\" class=\"data row3 col6\" >36.6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row4\" class=\"row_heading level0 row4\" >13</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow4_col0\" class=\"data row4 col0\" >USD/EUR</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow4_col1\" class=\"data row4 col1\" >CAD/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow4_col2\" class=\"data row4 col2\" >14.1%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow4_col3\" class=\"data row4 col3\" >34</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow4_col4\" class=\"data row4 col4\" >37.7%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow4_col5\" class=\"data row4 col5\" >31.9%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow4_col6\" class=\"data row4 col6\" >21.6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row5\" class=\"row_heading level0 row5\" >8</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow5_col0\" class=\"data row5 col0\" >USD/AUD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow5_col1\" class=\"data row5 col1\" >CAD/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow5_col2\" class=\"data row5 col2\" >14.5%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow5_col3\" class=\"data row5 col3\" >32</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow5_col4\" class=\"data row5 col4\" >50.4%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow5_col5\" class=\"data row5 col5\" >25.6%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow5_col6\" class=\"data row5 col6\" >21.6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row6\" class=\"row_heading level0 row6\" >24</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow6_col0\" class=\"data row6 col0\" >JPY/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow6_col1\" class=\"data row6 col1\" >MXN/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow6_col2\" class=\"data row6 col2\" >15.1%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow6_col3\" class=\"data row6 col3\" >58</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow6_col4\" class=\"data row6 col4\" >-10.1%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow6_col5\" class=\"data row6 col5\" >47.8%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow6_col6\" class=\"data row6 col6\" >36.6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row7\" class=\"row_heading level0 row7\" >2</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow7_col0\" class=\"data row7 col0\" >USD/GBP</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow7_col1\" class=\"data row7 col1\" >CAD/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow7_col2\" class=\"data row7 col2\" >15.8%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow7_col3\" class=\"data row7 col3\" >26</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow7_col4\" class=\"data row7 col4\" >29.2%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow7_col5\" class=\"data row7 col5\" >45.7%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow7_col6\" class=\"data row7 col6\" >21.6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row8\" class=\"row_heading level0 row8\" >3</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow8_col0\" class=\"data row8 col0\" >USD/GBP</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow8_col1\" class=\"data row8 col1\" >JPY/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow8_col2\" class=\"data row8 col2\" >15.9%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow8_col3\" class=\"data row8 col3\" >65</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow8_col4\" class=\"data row8 col4\" >-23.8%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow8_col5\" class=\"data row8 col5\" >45.7%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow8_col6\" class=\"data row8 col6\" >47.8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051flevel0_row9\" class=\"row_heading level0 row9\" >19</th>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow9_col0\" class=\"data row9 col0\" >CAD/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow9_col1\" class=\"data row9 col1\" >KRW/USD</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow9_col2\" class=\"data row9 col2\" >16.4%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow9_col3\" class=\"data row9 col3\" >23</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow9_col4\" class=\"data row9 col4\" >35.5%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow9_col5\" class=\"data row9 col5\" >21.6%</td>\n",
       "                        <td id=\"T_3eeb7a9e_2a4f_11eb_8195_00505612051frow9_col6\" class=\"data row9 col6\" >42.7%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2027440c7f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display_table = dual_binaries.sort_values('price').head(10)\n",
    "\n",
    "display_table['price'] = pd.Series(['{0:.1f}%'.format(val * 100) for val in display_table['price']], index=display_table.index)\n",
    "display_table['savings'] = pd.Series([int(val * 100) for val in display_table['savings']], index=display_table.index)\n",
    "\n",
    "display_table['corr'] = pd.Series(['{0:.1f}%'.format(val * 100) for val in display_table['corr']], index=display_table.index)\n",
    "\n",
    "display_table['individual_1'] = pd.Series(['{0:.1f}%'.format(val * 100) for val in display_table['individual_1']], index=display_table.index)\n",
    "display_table['individual_2'] = pd.Series(['{0:.1f}%'.format(val * 100) for val in display_table['individual_2']], index=display_table.index)\n",
    "\n",
    "display_table.style.background_gradient(subset=['savings'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Dual Binary Grids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We will now consider a wider range of strikes for the cheapest dual binary in\n",
    "the table above. Let us consider strikes within a 3% range from the targets\n",
    "defined above. The tables below show both the mid price and the implied correlation. Optimizing both of these outputs allows us to determine\n",
    "our preferable strikes for the trade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "min_price_index = dual_binaries['price'].idxmin()\n",
    "cross1 = dual_binaries.iloc[min_price_index]['cross1']\n",
    "cross2 = dual_binaries.iloc[min_price_index]['cross2']\n",
    "\n",
    "# % from target strikes to compute for the grid\n",
    "strikes = [-3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3]\n",
    "\n",
    "mcb_ppct_future = pd.DataFrame(index=strikes, columns=strikes)\n",
    "indiv_ppct_future = pd.DataFrame(index=strikes, columns=[cross1, cross2])\n",
    "\n",
    "with PricingContext(is_batch=True):\n",
    "    # loop over all dual binary combinations\n",
    "    for (i, j) in itertools.product(strikes, strikes):\n",
    "        strike1 = targets[cross1][0] * (1 + i / 100)\n",
    "        strike2 = targets[cross2][0] * (1 + j / 100)\n",
    "\n",
    "        # build the dual binary\n",
    "        legs = [\n",
    "            {'cross': cross1, 'strike': strike1, 'opt_type': targets[cross1][1]},\n",
    "            {'cross': cross2, 'strike': strike2, 'opt_type': targets[cross2][1]}\n",
    "        ]\n",
    "        mcb = build_dual_binary(tenor, legs)\n",
    "        mcb_ppct_future.loc[i, j] = mcb.price()\n",
    "\n",
    "    # loop over all indvidual binary combinations for both crosses\n",
    "    # we'll use these to compute savings later as well\n",
    "    for cross in [cross1, cross2]:\n",
    "        for i in strikes:\n",
    "            strike = targets[cross][0] * (1 + i / 100)\n",
    "            binary = build_binary(tenor, cross, strike, targets[cross][1])\n",
    "            indiv_ppct_future.loc[i, cross] = binary.price()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_matrix = pd.DataFrame(index=strikes, columns=strikes, dtype='float64')\n",
    "savings_matrix = pd.DataFrame(index=strikes, columns=strikes, dtype='float64')\n",
    "corr_matrix = pd.DataFrame(index=strikes, columns=strikes, dtype='float64')\n",
    "\n",
    "for (i, j) in itertools.product(strikes, strikes):\n",
    "    # price pct of the mcb\n",
    "    price_pct = mcb_ppct_future.loc[i, j].result() / 10e6\n",
    "    price_matrix.loc[i, j] = price_pct * 100\n",
    "\n",
    "    # calc savings    \n",
    "    p_1 = indiv_ppct_future.loc[i, cross1].result() / 10e6\n",
    "    p_2 = indiv_ppct_future.loc[j, cross2].result() / 10e6\n",
    "    savings_matrix.loc[i, j] = 1 - (price_pct / min(p_1, p_2))\n",
    "    \n",
    "    # calc correlation\n",
    "    corr_matrix.loc[i, j] = 100 * corr(price_pct, p_1, p_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quickly sanity check the percent prices of the individual binaries at the different strike combinations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CAD/USD</th>\n",
       "      <th>JPY/USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-3.0</th>\n",
       "      <td>3.4</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2.5</th>\n",
       "      <td>4.8</td>\n",
       "      <td>81.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2.0</th>\n",
       "      <td>6.6</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.5</th>\n",
       "      <td>9.1</td>\n",
       "      <td>71.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.0</th>\n",
       "      <td>12.3</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.5</th>\n",
       "      <td>16.4</td>\n",
       "      <td>56.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>21.6</td>\n",
       "      <td>47.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>27.7</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>34.8</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>42.6</td>\n",
       "      <td>23.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>50.8</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>58.7</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>65.9</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CAD/USD  JPY/USD\n",
       "-3.0      3.4     85.0\n",
       "-2.5      4.8     81.4\n",
       "-2.0      6.6     77.0\n",
       "-1.5      9.1     71.3\n",
       "-1.0     12.3     64.5\n",
       "-0.5     16.4     56.5\n",
       " 0.0     21.6     47.8\n",
       " 0.5     27.7     39.1\n",
       " 1.0     34.8     30.9\n",
       " 1.5     42.6     23.6\n",
       " 2.0     50.8     17.6\n",
       " 2.5     58.7     12.8\n",
       " 3.0     65.9      9.2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(indiv_ppct_future.applymap(lambda x: x.result() / 10e6) * 100, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's plot the mid price of the dual binary for all of the strike combinations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_heatmap(price_matrix, \"coolwarm\", cross2 + \" \" + targets[cross2][1],\n",
    "             cross1 + \" \" + targets[cross1][1], \"MCB Price (%) By Strikes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, let's plot the savings vs. the cheapest inividual binary for all of the combinations on the grid. Negative numbers indicate correlations that provide more savings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_heatmap(savings_matrix, \"Blues\", cross2 + \" \" + targets[cross2][1],\n",
    "             cross1 + \" \" + targets[cross1][1], \"Savings (%) By Strikes\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix - Dual Binary Carry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with PricingContext(is_batch=True):\n",
    "    for row in dual_binary_dict:\n",
    "        # build the dual binary\n",
    "        legs = [\n",
    "            {'cross': row['cross1'], 'strike': targets[row['cross1']][0], 'opt_type': targets[row['cross1']][1]},\n",
    "            {'cross': row['cross2'], 'strike': targets[row['cross2']][0], 'opt_type': targets[row['cross2']][1]}\n",
    "        ]\n",
    "        mcb = build_dual_binary('1m', legs)  # simple static carry, just reduce tenor to 1m\n",
    "\n",
    "        row['price_fwd_future'] = mcb.price()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cross1</th>\n",
       "      <th>cross2</th>\n",
       "      <th>price</th>\n",
       "      <th>savings</th>\n",
       "      <th>static_carry_1m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>JPY/USD</td>\n",
       "      <td>BRL/USD</td>\n",
       "      <td>0.201774</td>\n",
       "      <td>0.567274</td>\n",
       "      <td>-0.015183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>KRW/USD</td>\n",
       "      <td>BRL/USD</td>\n",
       "      <td>0.229853</td>\n",
       "      <td>0.461141</td>\n",
       "      <td>-0.017895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>JPY/USD</td>\n",
       "      <td>MXN/USD</td>\n",
       "      <td>0.150649</td>\n",
       "      <td>0.587894</td>\n",
       "      <td>-0.021028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>USD/GBP</td>\n",
       "      <td>BRL/USD</td>\n",
       "      <td>0.260522</td>\n",
       "      <td>0.429560</td>\n",
       "      <td>-0.022684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USD/GBP</td>\n",
       "      <td>KRW/USD</td>\n",
       "      <td>0.250200</td>\n",
       "      <td>0.413440</td>\n",
       "      <td>-0.025069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BRL/USD</td>\n",
       "      <td>MXN/USD</td>\n",
       "      <td>0.252088</td>\n",
       "      <td>0.310407</td>\n",
       "      <td>-0.025489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>USD/EUR</td>\n",
       "      <td>JPY/USD</td>\n",
       "      <td>0.087249</td>\n",
       "      <td>0.726459</td>\n",
       "      <td>-0.027515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USD/GBP</td>\n",
       "      <td>JPY/USD</td>\n",
       "      <td>0.159157</td>\n",
       "      <td>0.651511</td>\n",
       "      <td>-0.027681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>JPY/USD</td>\n",
       "      <td>KRW/USD</td>\n",
       "      <td>0.172747</td>\n",
       "      <td>0.595019</td>\n",
       "      <td>-0.028986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>USD/AUD</td>\n",
       "      <td>JPY/USD</td>\n",
       "      <td>0.074424</td>\n",
       "      <td>0.708740</td>\n",
       "      <td>-0.030358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cross1   cross2     price   savings  static_carry_1m\n",
       "23  JPY/USD  BRL/USD  0.201774  0.567274        -0.015183\n",
       "25  KRW/USD  BRL/USD  0.229853  0.461141        -0.017895\n",
       "24  JPY/USD  MXN/USD  0.150649  0.587894        -0.021028\n",
       "5   USD/GBP  BRL/USD  0.260522  0.429560        -0.022684\n",
       "4   USD/GBP  KRW/USD  0.250200  0.413440        -0.025069\n",
       "27  BRL/USD  MXN/USD  0.252088  0.310407        -0.025489\n",
       "14  USD/EUR  JPY/USD  0.087249  0.726459        -0.027515\n",
       "3   USD/GBP  JPY/USD  0.159157  0.651511        -0.027681\n",
       "22  JPY/USD  KRW/USD  0.172747  0.595019        -0.028986\n",
       "9   USD/AUD  JPY/USD  0.074424  0.708740        -0.030358"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for row in dual_binary_dict:\n",
    "    row['price_fwd'] = row['price_fwd_future'].result() / 10e6\n",
    "    row['static_carry_1m'] = row['price_fwd'] - row['price']\n",
    "\n",
    "carry_table = pd.DataFrame(dual_binary_dict).sort_values('static_carry_1m', ascending=False)\n",
    "carry_table.head(10)[['cross1', 'cross2', 'price', 'savings', 'static_carry_1m']]"
   ]
  },
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    "### Disclaimers\n",
    "\n",
    "Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\n",
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    "OTC Derivatives Risk Disclosures: \n",
    "Terms of the Transaction: To understand clearly the terms and conditions of any OTC derivative transaction you may enter into, you should carefully review the Master Agreement, including any related schedules, credit support documents, addenda and exhibits. You should not enter into OTC derivative transactions unless you understand the terms of the transaction you are entering into as well as the nature and extent of your risk exposure. You should also be satisfied that the OTC derivative transaction is appropriate for you in light of your circumstances and financial condition. You may be requested to post margin or collateral to support written OTC derivatives at levels consistent with the internal policies of Goldman Sachs. \n",
    "\n",
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    "\n",
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    "\n",
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    "\n",
    "Early Termination Payments: The provisions of an OTC Derivative Transaction may allow for early termination and, in such cases, either you or Goldman Sachs may be required to make a potentially significant termination payment depending upon whether the OTC Derivative Transaction is in-the-money to Goldman Sachs or you at the time of termination. Indexes: Goldman Sachs does not warrant, and takes no responsibility for, the structure, method of computation or publication of any currency exchange rates, interest rates, indexes of such rates, or credit, equity or other indexes, unless Goldman Sachs specifically advises you otherwise.\n",
    "Risk Disclosure Regarding futures, options, equity swaps, and other derivatives as well as non-investment-grade securities and ADRs: Please ensure that you have read and understood the current options, futures and security futures disclosure document before entering into any such transactions. Current United States listed options, futures and security futures disclosure documents are available from our sales representatives or at http://www.theocc.com/components/docs/riskstoc.pdf,  http://www.goldmansachs.com/disclosures/risk-disclosure-for-futures.pdf and https://www.nfa.futures.org/investors/investor-resources/files/security-futures-disclosure.pdf, respectively. Certain transactions - including those involving futures, options, equity swaps, and other derivatives as well as non-investment-grade securities - give rise to substantial risk and are not available to nor suitable for all investors. If you have any questions about whether you are eligible to enter into these transactions with Goldman Sachs, please contact your sales representative. Foreign-currency-denominated securities are subject to fluctuations in exchange rates that could have an adverse effect on the value or price of, or income derived from, the investment. In addition, investors in securities such as ADRs, the values of which are influenced by foreign currencies, effectively assume currency risk.\n",
    "Options Risk Disclosures: Options may trade at a value other than that which may be inferred from the current levels of interest rates, dividends (if applicable) and the underlier due to other factors including, but not limited to, expectations of future levels of interest rates, future levels of dividends and the volatility of the underlier at any time prior to maturity. Note: Options involve risk and are not suitable for all investors. Please ensure that you have read and understood the current options disclosure document before entering into any standardized options transactions. United States listed options disclosure documents are available from our sales representatives or at http://theocc.com/publications/risks/riskstoc.pdf. A secondary market may not be available for all options. Transaction costs may be a significant factor in option strategies calling for multiple purchases and sales of options, such as spreads. When purchasing long options an investor may lose their entire investment and when selling uncovered options the risk is potentially unlimited. Supporting documentation for any comparisons, recommendations, statistics, technical data, or other similar information will be supplied upon request.\n",
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